diff --git a/dbgsom/SomVQ.py b/dbgsom/SomVQ.py
index a495141..cb38133 100644
--- a/dbgsom/SomVQ.py
+++ b/dbgsom/SomVQ.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 import numpy.typing as npt
-from sklearn.base import ClusterMixin, TransformerMixin, check_array
+from sklearn.base import ClusterMixin, TransformerMixin, check_array, check_is_fitted
 
 from .BaseSom import BaseSom
 
@@ -117,10 +117,26 @@ def _prepare_inputs(self, X: npt.ArrayLike, y=None) -> tuple[npt.NDArray, None]:
         X = check_array(array=X, ensure_min_samples=4, dtype=[np.float64, np.float32])
         return X, y
 
-    def _predict(self, X: npt.ArrayLike) -> npt.NDArray:
-        labels = self._get_winning_neurons(X, n_bmu=1)
-        return labels
-
     def _label_prototypes(self, X: npt.ArrayLike, y=None) -> None:
         for i, neuron in enumerate(self.som_):
             self.som_.nodes[neuron]["label"] = i
+
+    def predict(self, X: npt.ArrayLike) -> np.ndarray:
+        """Predict the closest neuron each sample in X belongs to.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            New data to predict.
+
+        Returns
+        -------
+        labels : ndarray of shape (n_samples,)
+            If fitted unsupervised: Index of best matching prototype.
+
+        """
+        check_is_fitted(self)
+        X = check_array(X)
+        labels = self._get_winning_neurons(X, n_bmu=1)
+
+        return labels
diff --git a/profile b/profile
deleted file mode 100644
index 64bf7ec..0000000
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